diff --git a/_images/Precip_Accumulation-Region_Of_Interest_7_1.png b/_images/Precip_Accumulation-Region_Of_Interest_7_1.png deleted file mode 100644 index 2add4db..0000000 Binary files a/_images/Precip_Accumulation-Region_Of_Interest_7_1.png and /dev/null differ diff --git a/_images/Precip_Accumulation-Region_Of_Interest_9_1.png b/_images/Precip_Accumulation-Region_Of_Interest_9_1.png deleted file mode 100644 index 48a9ce3..0000000 Binary files a/_images/Precip_Accumulation-Region_Of_Interest_9_1.png and /dev/null differ diff --git a/_images/Precip_Accumulation_Region_of_Interest_27_1.png b/_images/Precip_Accumulation_Region_of_Interest_27_1.png new file mode 100644 index 0000000..99230b1 Binary files /dev/null and b/_images/Precip_Accumulation_Region_of_Interest_27_1.png differ diff --git a/_images/Precip_Accumulation_Region_of_Interest_29_1.png b/_images/Precip_Accumulation_Region_of_Interest_29_1.png new file mode 100644 index 0000000..a5cbacc Binary files /dev/null and b/_images/Precip_Accumulation_Region_of_Interest_29_1.png differ diff --git a/_images/Precip_Accumulation_Region_of_Interest_37_2.png b/_images/Precip_Accumulation_Region_of_Interest_37_2.png new file mode 100644 index 0000000..ac7027e Binary files /dev/null and b/_images/Precip_Accumulation_Region_of_Interest_37_2.png differ diff --git a/_sources/examples/generated/Precip_Accumulation-Region_Of_Interest.rst.txt b/_sources/examples/generated/Precip_Accumulation-Region_Of_Interest.rst.txt deleted file mode 100644 index 11b965b..0000000 --- a/_sources/examples/generated/Precip_Accumulation-Region_Of_Interest.rst.txt +++ /dev/null @@ -1,215 +0,0 @@ -====================================== -Precip Accumulation-Region Of Interest -====================================== -`Notebook `_ -A way to determine areas of greatest precipitation and generate imagery -for that sector. - -.. code:: ipython3 - - from awips.dataaccess import DataAccessLayer - import cartopy.crs as ccrs - import matplotlib.pyplot as plt - from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER - from metpy.units import units - import numpy as np - from shapely.geometry import Point, Polygon - - %matplotlib inline - - conus=[-120, -65, 28, 50] - conus_envelope = Polygon([(conus[0],conus[2]),(conus[0],conus[3]), - (conus[1],conus[3]),(conus[1],conus[2]), - (conus[0],conus[2])]) - - DataAccessLayer.changeEDEXHost("edex-cloud.unidata.ucar.edu") - request = DataAccessLayer.newDataRequest("grid", envelope=conus_envelope) - request.setLocationNames("NAM40") - request.setLevels("0.0SFC") - request.setParameters("TP") - - cycles = DataAccessLayer.getAvailableTimes(request, True) - times = DataAccessLayer.getAvailableTimes(request) - fcstRun = DataAccessLayer.getForecastRun(cycles[-2], times) - -Calculate accumulated precipitation - -.. code:: ipython3 - - for i, tt in enumerate(fcstRun): - response = DataAccessLayer.getGridData(request, [tt]) - grid = response[0] - if i>0: - data += grid.getRawData() - else: - data = grid.getRawData() - data[data <= -9999] = 0 - print(data.min(), data.max(), grid.getDataTime().getFcstTime()/3600) - - - lons, lats = grid.getLatLonCoords() - bbox = [lons.min(), lons.max(), lats.min(), lats.max()] - fcstHr = int(grid.getDataTime().getFcstTime()/3600) - - tp_inch = data * (0.0393701) - print(tp_inch.min(), tp_inch.max()) - - -.. parsed-literal:: - - 0.0 0.0 0.0 - 0.0 32.1875 3.0 - 0.0 52.125 6.0 - 0.0 74.375 9.0 - 0.0 77.125 12.0 - 0.0 78.625 15.0 - 0.0 78.75 18.0 - 0.0 78.75 21.0 - 0.0 79.375 24.0 - 0.0 82.25 27.0 - 0.0 84.0 30.0 - 0.0 84.6875 33.0 - 0.0 85.625 36.0 - 0.0 87.3125 39.0 - 0.0 87.75 42.0 - 0.0 87.75 45.0 - 0.0 89.375 48.0 - 0.0 127.875 51.0 - 0.0 139.5625 54.0 - 0.0 139.6875 57.0 - 0.0 140.5625 60.0 - 0.0 140.625 63.0 - 0.0 140.625 66.0 - 0.0 140.625 69.0 - 0.0 140.625 72.0 - 0.0 140.625 75.0 - 0.0 140.625 78.0 - 0.0 140.625 81.0 - 0.0 140.625 84.0 - 0.0 5.5364203 - - -Determine lat/lon of maximum rainfall value: - -.. code:: ipython3 - - ii,jj = np.where(tp_inch==tp_inch.max()) - i=ii[0] - j=jj[0] - point = Point(lons[i][j], lats[i][j]) - -Draw CONUS map - -.. code:: ipython3 - - def make_map(bbox, projection=ccrs.PlateCarree()): - fig, ax = plt.subplots(figsize=(20, 14), - subplot_kw=dict(projection=projection)) - ax.set_extent(bbox) - ax.coastlines(resolution='50m') - return fig, ax - - cmap = plt.get_cmap('rainbow') - fig, ax = make_map(bbox=bbox) - cs = ax.pcolormesh(lons, lats, tp_inch, cmap=cmap) - cbar = fig.colorbar(cs, shrink=0.7, orientation='horizontal') - cbar.set_label(grid.getLocationName() + " Total precipitation in inches, " \ - + str(fcstHr) + "-hr fcst valid " + str(grid.getDataTime().getRefTime())) - - ax.scatter(point.x, point.y, s=300, - transform=ccrs.PlateCarree(),marker="+",facecolor='black') - - inc = 3.5 - box=[point.x-inc, point.x+inc, point.y-inc, point.y+inc] - polygon = Polygon([(box[0],box[2]),(box[0],box[3]), - (box[1],box[3]),(box[1],box[2]), - (box[0],box[2])]) - ax.add_geometries([polygon], ccrs.PlateCarree(), facecolor='none', edgecolor='white', linewidth=2) - - - - -.. parsed-literal:: - - - - - - -.. image:: Precip_Accumulation-Region_Of_Interest_files/Precip_Accumulation-Region_Of_Interest_7_1.png - - -Now create a new gridded data request with a geometry envelope for our -Region of Interest - -.. code:: ipython3 - - request = DataAccessLayer.newDataRequest("grid", envelope=polygon) - request.setLocationNames("HRRR") - request.setLevels("0.0SFC") - request.setParameters("TP") - - cycles = DataAccessLayer.getAvailableTimes(request, True) - times = DataAccessLayer.getAvailableTimes(request) - fcstRun = DataAccessLayer.getForecastRun(cycles[-2], times) - - - for i, tt in enumerate(fcstRun): - response = DataAccessLayer.getGridData(request, [tt]) - grid = response[0] - if i>0: - data += grid.getRawData() - else: - data = grid.getRawData() - data[data <= -9999] = 0 - print(data.min(), data.max(), grid.getDataTime().getFcstTime()/3600) - - - lons, lats = grid.getLatLonCoords() - bbox = [lons.min(), lons.max(), lats.min(), lats.max()] - fcstHr = int(grid.getDataTime().getFcstTime()/3600) - - tp_inch = data * (0.0393701) - print(tp_inch.min(), tp_inch.max()) - - def make_map(bbox, projection=ccrs.PlateCarree()): - fig, ax = plt.subplots(figsize=(20, 14), - subplot_kw=dict(projection=projection)) - ax.set_extent(bbox) - ax.coastlines(resolution='50m') - return fig, ax - - cmap = plt.get_cmap('rainbow') - fig, ax = make_map(bbox=box) - cs = ax.pcolormesh(lons, lats, tp_inch, cmap=cmap) - cbar = fig.colorbar(cs, shrink=0.7, orientation='horizontal') - cbar.set_label(grid.getLocationName() + " Total precipitation in inches, " \ - + str(fcstHr) + "-hr fcst valid " + str(grid.getDataTime().getRefTime())) - - -.. parsed-literal:: - - 0.0 1.853 1.0 - 0.0 3.5290003 2.0 - 0.0 5.0290003 3.0 - 0.0 5.051 4.0 - 0.0 5.2960005 5.0 - 0.0 5.2960005 6.0 - 0.0 5.8269997 7.0 - 0.0 6.1790004 8.0 - 0.0 6.1890006 9.0 - 0.0 9.071 10.0 - 0.0 10.812 11.0 - 0.0 14.718 12.0 - 0.0 18.295 13.0 - 0.0 21.339 14.0 - 0.0 22.626 15.0 - 0.0 28.670002 16.0 - 0.0 32.334 17.0 - 0.0 36.628002 18.0 - 0.0 1.4420482 - - - -.. image:: Precip_Accumulation-Region_Of_Interest_files/Precip_Accumulation-Region_Of_Interest_9_1.png - diff --git a/_sources/examples/generated/Precip_Accumulation_Region_of_Interest.rst.txt b/_sources/examples/generated/Precip_Accumulation_Region_of_Interest.rst.txt new file mode 100644 index 0000000..a47a729 --- /dev/null +++ b/_sources/examples/generated/Precip_Accumulation_Region_of_Interest.rst.txt @@ -0,0 +1,479 @@ +====================================== +Precip Accumulation Region of Interest +====================================== +`Notebook `_ +Python-AWIPS Tutorial Notebook + +-------------- + +Objectives +========== + +- Access the model data from an EDEX server and limit the data returned + by using model specific parameters +- Calculate the total precipitation over several model runs +- Create a colorized plot for the continental US of the accumulated + precipitation data +- Calculate and identify area of highest of precipitation +- Use higher resolution data to draw region of interest + +-------------- + +Table of Contents +----------------- + +| `1 + Imports `__\ +| `2 Initial + Setup `__\ +|     `2.1 Geographic + Filter `__\ +|     `2.2 EDEX + Connnection `__\ +|     `2.3 Refine the + Request `__\ +|     `2.4 Get + Times `__\ +| `3 Function: + calculate_accumulated_precip() `__\ +| `4 Function: + make_map() `__\ +| `5 Get the + Data! `__\ +| `6 Plot the + Data! `__\ +|     `6.1 Create CONUS + Image `__\ +|     `6.2 Create Region of Interest + Image `__\ +| `7 High Resolution + ROI `__\ +|     `7.1 New Data + Request `__\ +|     `7.2 Calculate + Data `__\ +|     `7.3 Plot + ROI `__\ +| `8 See + Also `__\ +|     `8.1 Related + Notebooks `__\ +|     `8.2 Additional + Documentation `__\ + +1 Imports +--------- + +The imports below are used throughout the notebook. Note the first +import is coming directly from python-awips and allows us to connect to +an EDEX server. The subsequent imports are for data manipulation and +visualization. + +.. code:: ipython3 + + from awips.dataaccess import DataAccessLayer + import cartopy.crs as ccrs + import matplotlib.pyplot as plt + from metpy.units import units + import numpy as np + from shapely.geometry import Point, Polygon + +`Top `__ + +-------------- + +2 Initial Setup +--------------- + +2.1 Geographic Filter +~~~~~~~~~~~~~~~~~~~~~ + +By defining a bounding box for the Continental US (CONUS), we’re able to +optimize the data request sent to the EDEX server. + +.. code:: ipython3 + + conus=[-125, -65, 25, 55] + conus_envelope = Polygon([(conus[0],conus[2]),(conus[0],conus[3]), + (conus[1],conus[3]),(conus[1],conus[2]), + (conus[0],conus[2])]) + +2.2 EDEX Connection +~~~~~~~~~~~~~~~~~~~ + +First we establish a connection to Unidata’s public EDEX server. With +that connection made, we can create a `new data request +object `__ +and set the data type to **grid**, and use the geographic envelope we +just created. + +.. code:: ipython3 + + DataAccessLayer.changeEDEXHost("edex-cloud.unidata.ucar.edu") + request = DataAccessLayer.newDataRequest("grid", envelope=conus_envelope) + +2.3 Refine the Request +~~~~~~~~~~~~~~~~~~~~~~ + +Here we specify which model we’re interested in by setting the +*LocationNames*, and the specific data we’re interested in by setting +the *Levels* and *Parameters*. + +.. code:: ipython3 + + request.setLocationNames("GFS1p0") + request.setLevels("0.0SFC") + request.setParameters("TP") + +2.4 Get Times +~~~~~~~~~~~~~ + +We need to get the available times and cycles for our model data + +.. code:: ipython3 + + cycles = DataAccessLayer.getAvailableTimes(request, True) + times = DataAccessLayer.getAvailableTimes(request) + fcstRun = DataAccessLayer.getForecastRun(cycles[-1], times) + +`Top `__ + +-------------- + +3 Function: calculate_accumulated_precip() +------------------------------------------ + +Since we’ll want to calculate the accumulated precipitation of our data +more than once, it makes sense to create a function that we can call +instead of duplicating the logic. + +This function cycles through all the grid data responses and adds up all +of the rainfall to produce a numpy array with the total ammount of +rainfall for the given data request. It also finds the maximum rainfall +point in x and y coordinates. + +.. code:: ipython3 + + def calculate_accumulated_precip(dataRequest, forecastRun): + + for i, tt in enumerate(forecastRun): + response = DataAccessLayer.getGridData(dataRequest, [tt]) + grid = response[0] + if i>0: + data += grid.getRawData() + else: + data = grid.getRawData() + data[data <= -9999] = 0 + print(data.min(), data.max(), grid.getDataTime().getFcstTime()/3600) + + # Convert from mm to inches + result = data * (0.0393701) + + ii,jj = np.where(result==result.max()) + i=ii[0] + j=jj[0] + + return result, i, j + +`Top `__ + +-------------- + +4 Fuction: make_map() +--------------------- + +This function creates the basics of the map we’re going to plot our data +on. It takes in a bounding box to determine the extent and then adds +coastlines for easy frame of reference. + +.. code:: ipython3 + + def make_map(bbox, projection=ccrs.PlateCarree()): + fig, ax = plt.subplots(figsize=(20, 14), + subplot_kw=dict(projection=projection)) + ax.set_extent(bbox) + ax.coastlines(resolution='50m') + return fig, ax + +`Top `__ + +-------------- + +5 Get the Data! +--------------- + +Access the data from the DataAccessLayer interface using the +*getGridData* function. Use that data to calculate the accumulated +rainfall, the maximum rainfall point, and the region of interest +bounding box. + +.. code:: ipython3 + + ## get the grid response from edex + response = DataAccessLayer.getGridData(request, [fcstRun[-1]]) + ## take the first result to get the location information from + grid = response[0] + + ## get the location coordinates and create a bounding box for our map + lons, lats = grid.getLatLonCoords() + bbox = [lons.min(), lons.max(), lats.min(), lats.max()] + fcstHr = int(grid.getDataTime().getFcstTime()/3600) + + ## calculate the total precipitation + tp_inch, i, j = calculate_accumulated_precip(request, fcstRun) + print(tp_inch.min(), tp_inch.max()) + + ## use the max points coordinates to get the max point in lat/lon coords + maxPoint = Point(lons[i][j], lats[i][j]) + inc = 3.5 + ## create a region of interest bounding box + roi_box=[maxPoint.x-inc, maxPoint.x+inc, maxPoint.y-inc, maxPoint.y+inc] + roi_polygon = Polygon([(roi_box[0],roi_box[2]),(roi_box[0],roi_box[3]), + (roi_box[1],roi_box[3]),(roi_box[1],roi_box[2]),(roi_box[0],roi_box[2])]) + + print(maxPoint) + + +.. parsed-literal:: + + 0.0 10.0625 6.0 + 0.0 21.75 12.0 + 0.0 35.1875 18.0 + 0.0 43.5 24.0 + 0.0 45.5625 42.0 + 0.0 47.9375 48.0 + 0.0 52.0625 54.0 + 0.0 56.375 60.0 + 0.0 86.625 66.0 + 0.0 92.4375 72.0 + 0.0 94.375 78.0 + 0.0 95.375 84.0 + 0.0 98.3125 90.0 + 0.0 100.125 96.0 + 0.0 101.6875 102.0 + 0.0 104.0 108.0 + 0.0 107.1875 114.0 + 0.0 115.25 120.0 + 0.0 129.0 126.0 + 0.0 136.375 132.0 + 0.0 141.125 138.0 + 0.0 145.25 144.0 + 0.0 147.375 150.0 + 0.0 5.802169 + POINT (-124 42) + + +`Top `__ + +-------------- + +6 Plot the Data! +---------------- + +6.1 Create CONUS Image +~~~~~~~~~~~~~~~~~~~~~~ + +Plot our data on our CONUS map. + +.. code:: ipython3 + + cmap = plt.get_cmap('rainbow') + fig, ax = make_map(bbox=bbox) + cs = ax.pcolormesh(lons, lats, tp_inch, cmap=cmap) + cbar = fig.colorbar(cs, shrink=0.7, orientation='horizontal') + cbar.set_label(grid.getLocationName() + " Total precipitation in inches, " \ + + str(fcstHr) + "-hr fcst valid " + str(grid.getDataTime().getRefTime())) + + ax.scatter(maxPoint.x, maxPoint.y, s=300, + transform=ccrs.PlateCarree(),marker="+",facecolor='black') + + ax.add_geometries([roi_polygon], ccrs.PlateCarree(), facecolor='none', edgecolor='white', linewidth=2) + + + + +.. parsed-literal:: + + + + + + +.. image:: Precip_Accumulation_Region_of_Interest_files/Precip_Accumulation_Region_of_Interest_27_1.png + + +6.2 Create Region of Interest Image +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Now crop the data and zoom in on the region of interest (ROI) to create +a new plot. + +.. code:: ipython3 + + # cmap = plt.get_cmap('rainbow') + fig, ax = make_map(bbox=roi_box) + + cs = ax.pcolormesh(lons, lats, tp_inch, cmap=cmap) + cbar = fig.colorbar(cs, shrink=0.7, orientation='horizontal') + cbar.set_label(grid.getLocationName() + " Total precipitation in inches, " \ + + str(fcstHr) + "-hr fcst valid " + str(grid.getDataTime().getRefTime())) + + ax.scatter(maxPoint.x, maxPoint.y, s=300, + transform=ccrs.PlateCarree(),marker="+",facecolor='black') + + + + +.. parsed-literal:: + + + + + + +.. image:: Precip_Accumulation_Region_of_Interest_files/Precip_Accumulation_Region_of_Interest_29_1.png + + +`Top `__ + +-------------- + +7 High Resolution ROI +--------------------- + +7.1 New Data Request +~~~~~~~~~~~~~~~~~~~~ + +To see the region of interest more clearly, we can redo the process with +a higher resolution model (GFS20 vs. GFS1.0). + +.. code:: ipython3 + + roiRequest = DataAccessLayer.newDataRequest("grid", envelope=conus_envelope) + roiRequest.setLocationNames("GFS20") + roiRequest.setLevels("0.0SFC") + roiRequest.setParameters("TP") + + roiCycles = DataAccessLayer.getAvailableTimes(roiRequest, True) + roiTimes = DataAccessLayer.getAvailableTimes(roiRequest) + roiFcstRun = DataAccessLayer.getForecastRun(roiCycles[-1], roiTimes) + +7.2 Calculate Data +~~~~~~~~~~~~~~~~~~ + +.. code:: ipython3 + + roiResponse = DataAccessLayer.getGridData(roiRequest, [roiFcstRun[-1]]) + print(roiResponse) + roiGrid = roiResponse[0] + + roiLons, roiLats = roiGrid.getLatLonCoords() + + roi_data, i, j = calculate_accumulated_precip(roiRequest, roiFcstRun) + + roiFcstHr = int(roiGrid.getDataTime().getFcstTime()/3600) + + +.. parsed-literal:: + + [] + 0.0 22.5625 3.0 + 0.0 35.375 6.0 + 0.0 38.375 9.0 + 0.0 38.375 12.0 + 0.0 41.375 15.0 + 0.0 48.625 18.0 + 0.0 73.0625 30.0 + 0.0 94.9375 33.0 + 0.0 96.125 36.0 + 0.0 97.0 39.0 + 0.0 99.375 45.0 + 0.0 100.0625 48.0 + 0.0 100.25 51.0 + 0.0 100.4375 57.0 + 0.0 100.4375 63.0 + 0.0 118.25 66.0 + 0.0 127.625 69.0 + 0.0 131.125 75.0 + 0.0 131.375 78.0 + 0.0 131.5 81.0 + 0.0 131.875 84.0 + 0.0 132.875 90.0 + 0.0 133.375 96.0 + 0.0 139.1875 102.0 + 0.0 141.625 120.0 + 0.0 141.75 126.0 + 0.0 142.1875 132.0 + 0.0 143.375 138.0 + 0.0 148.6875 144.0 + 0.0 156.25 150.0 + + +7.3 Plot ROI +~~~~~~~~~~~~ + +.. code:: ipython3 + + # cmap = plt.get_cmap('rainbow') + fig, ax = make_map(bbox=roi_box) + + cs = ax.pcolormesh(roiLons, roiLats, roi_data, cmap=cmap) + cbar = fig.colorbar(cs, shrink=0.7, orientation='horizontal') + cbar.set_label(roiGrid.getLocationName() + " Total precipitation in inches, " \ + + str(roiFcstHr) + "-hr fcst valid " + str(roiGrid.getDataTime().getRefTime())) + + ax.scatter(maxPoint.x, maxPoint.y, s=300, + transform=ccrs.PlateCarree(),marker="+",facecolor='black') + + +.. parsed-literal:: + + /Users/scarter/opt/miniconda3/envs/python3-awips/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:1702: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh. + X, Y, C, shading = self._pcolorargs('pcolormesh', *args, + + + + +.. parsed-literal:: + + + + + + +.. image:: Precip_Accumulation_Region_of_Interest_files/Precip_Accumulation_Region_of_Interest_37_2.png + + +`Top `__ + +-------------- + +8 See Also +---------- + +8.1 Related Notebooks +~~~~~~~~~~~~~~~~~~~~~ + +- `Colorized Grid + Data `__ +- `Grid Levels and + Parameters `__ + +8.2 Additional Documentation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +**python-awips:** \* +`awips.DataAccessLayer `__ +\* +`awips.PyGridData `__ + +**matplotlib:** \* +`matplotlib.pyplot `__ +\* +`matplotlib.pyplot.subplot `__ +\* +`matplotlib.pyplot.pcolormesh `__ + +`Top `__ + +-------------- diff --git a/examples/generated/Colored_Surface_Temperature_Plot.html b/examples/generated/Colored_Surface_Temperature_Plot.html index 6788276..4dfb3c8 100644 --- a/examples/generated/Colored_Surface_Temperature_Plot.html +++ b/examples/generated/Colored_Surface_Temperature_Plot.html @@ -72,7 +72,7 @@
  • Map Resources and Topography
  • Model Sounding Data
  • NEXRAD Level3 Radar
    • -
  • Precip Accumulation-Region Of Interest
    • +
  • Precip Accumulation Region of Interest
  • Regional Surface Obs Plot
  • Satellite Imagery
  • Upper Air BUFR Soundings
    • diff --git a/examples/generated/Colorized_Grid_Data.html b/examples/generated/Colorized_Grid_Data.html index cd4be8b..ac15fd1 100644 --- a/examples/generated/Colorized_Grid_Data.html +++ b/examples/generated/Colorized_Grid_Data.html @@ -71,7 +71,7 @@
  • Map Resources and Topography
  • Model Sounding Data
  • NEXRAD Level3 Radar
    • -
  • Precip Accumulation-Region Of Interest
    • +
  • Precip Accumulation Region of Interest
  • Regional Surface Obs Plot
  • Satellite Imagery
  • Upper Air BUFR Soundings
    • diff --git a/examples/generated/Forecast_Model_Vertical_Sounding.html b/examples/generated/Forecast_Model_Vertical_Sounding.html index fe3d0c1..532dc0b 100644 --- a/examples/generated/Forecast_Model_Vertical_Sounding.html +++ b/examples/generated/Forecast_Model_Vertical_Sounding.html @@ -75,7 +75,7 @@
  • Map Resources and Topography
  • Model Sounding Data
  • NEXRAD Level3 Radar
    • -
  • Precip Accumulation-Region Of Interest
    • +
  • Precip Accumulation Region of Interest
  • Regional Surface Obs Plot
  • Satellite Imagery
  • Upper Air BUFR Soundings
    • diff --git a/examples/generated/GOES_CIRA_Product_Writer.html b/examples/generated/GOES_CIRA_Product_Writer.html index 78c7075..f748f68 100644 --- a/examples/generated/GOES_CIRA_Product_Writer.html +++ b/examples/generated/GOES_CIRA_Product_Writer.html @@ -71,7 +71,7 @@
  • Map Resources and Topography
  • Model Sounding Data
  • NEXRAD Level3 Radar
    • -
  • Precip Accumulation-Region Of Interest
    • +
  • Precip Accumulation Region of Interest
  • Regional Surface Obs Plot
  • Satellite Imagery
  • Upper Air BUFR Soundings
    • diff --git a/examples/generated/Grid_Levels_and_Parameters.html b/examples/generated/Grid_Levels_and_Parameters.html index 5429602..8aa512d 100644 --- a/examples/generated/Grid_Levels_and_Parameters.html +++ b/examples/generated/Grid_Levels_and_Parameters.html @@ -75,7 +75,7 @@
  • Map Resources and Topography
  • Model Sounding Data
  • NEXRAD Level3 Radar
    • -
  • Precip Accumulation-Region Of Interest
    • +
  • Precip Accumulation Region of Interest
  • Regional Surface Obs Plot
  • Satellite Imagery
  • Upper Air BUFR Soundings
    • diff --git a/examples/generated/METAR_Station_Plot_with_MetPy.html b/examples/generated/METAR_Station_Plot_with_MetPy.html index 624c051..15699dd 100644 --- a/examples/generated/METAR_Station_Plot_with_MetPy.html +++ b/examples/generated/METAR_Station_Plot_with_MetPy.html @@ -72,7 +72,7 @@
  • Map Resources and Topography
  • Model Sounding Data
  • NEXRAD Level3 Radar
    • -
  • Precip Accumulation-Region Of Interest
    • +
  • Precip Accumulation Region of Interest
  • Regional Surface Obs Plot
  • Satellite Imagery
  • Upper Air BUFR Soundings
    • diff --git a/examples/generated/Map_Resources_and_Topography.html b/examples/generated/Map_Resources_and_Topography.html index 5f02655..4ba78eb 100644 --- a/examples/generated/Map_Resources_and_Topography.html +++ b/examples/generated/Map_Resources_and_Topography.html @@ -76,7 +76,7 @@
  • Model Sounding Data
  • NEXRAD Level3 Radar
    • -
  • Precip Accumulation-Region Of Interest
    • +
  • Precip Accumulation Region of Interest
  • Regional Surface Obs Plot
  • Satellite Imagery
  • Upper Air BUFR Soundings
    • diff --git a/examples/generated/Model_Sounding_Data.html b/examples/generated/Model_Sounding_Data.html index b8c3a73..782ff1e 100644 --- a/examples/generated/Model_Sounding_Data.html +++ b/examples/generated/Model_Sounding_Data.html @@ -73,7 +73,7 @@
  • NEXRAD Level3 Radar
    • -
  • Precip Accumulation-Region Of Interest
    • +
  • Precip Accumulation Region of Interest
  • Regional Surface Obs Plot
  • Satellite Imagery
  • Upper Air BUFR Soundings
    • diff --git a/examples/generated/NEXRAD_Level3_Radar.html b/examples/generated/NEXRAD_Level3_Radar.html index 7808db4..26db830 100644 --- a/examples/generated/NEXRAD_Level3_Radar.html +++ b/examples/generated/NEXRAD_Level3_Radar.html @@ -20,7 +20,7 @@ - + @@ -59,7 +59,7 @@
  • Map Resources and Topography
  • Model Sounding Data
  • NEXRAD Level3 Radar
    • -
  • Precip Accumulation-Region Of Interest
    • +
  • Precip Accumulation Region of Interest
  • Regional Surface Obs Plot
  • Satellite Imagery
  • Upper Air BUFR Soundings
    • @@ -368,7 +368,7 @@